class_docstr: A replay buffer shard for multiagent setups.
  
  This buffer is meant to be run in parallel to distribute experiences
  across `num_shards` shards. Unlike simpler buffers, it holds a set of
  buffers - one for each policy ID.
docstr: Restores all local state to the provided `state`.
  
  Args:
      state: The new state to set this buffer. Can be obtained by
          calling `self.get_state()`.
docstr_tok: ["Restores","all","local","state","to","the","provided","`","state","`",".","Args",":","state",":","The","new","state","to","set","this","buffer",".","Can","be","obtained","by","calling","`","self.get_state","(",")","`","."]
code: def set_state(self, state: Dict[str, Any]) -> None:
      """Restores all local state to the provided `state`.
  
      Args:
          state: The new state to set this buffer. Can be obtained by
              calling `self.get_state()`.
      """
      self._num_added = state["num_added"]
      buffer_states = state["replay_buffers"]
      for policy_id in buffer_states.keys():
          self.replay_buffers[policy_id].set_state(
              buffer_states[policy_id]
          )
  
imports: import collections
  import logging
  from enum import Enum
  from typing import Any, Dict, Optional
  from ray.util.timer import _Timer
  from ray.rllib.policy.rnn_sequencing import timeslice_along_seq_lens_with_overlap
  from ray.rllib.policy.sample_batch import MultiAgentBatch, SampleBatch
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.deprecation import Deprecated
  from ray.rllib.utils.from_config import from_config
  from ray.rllib.utils.replay_buffers.replay_buffer import _ALL_POLICIES, ReplayBuffer, StorageUnit
  from ray.rllib.utils.typing import PolicyID, SampleBatchType
  from ray.util.annotations import DeveloperAPI
  from ray.util.debug import log_once
label_desc: Use image node_id 9 for calling the MultiAgentReplayBuffer obj's underlying member method code with example usage: obj.set_state(state) without return types